code: def __init__(self, cfg, local_rank=0):
      super(JMLRInference, self).__init__()
      backbone = get_network(cfg)
      if cfg.ckpt is None:
          ckpts = list(glob.glob(osp.join(cfg.output, "backbone*.pth")))
          backbone_pth = sorted(ckpts)[-1]
      else:
          backbone_pth = cfg.ckpt
      if local_rank == 0:
          print(backbone_pth)
      backbone_ckpt = torch.load(
          backbone_pth, map_location=torch.device(local_rank)
      )
      if "model" in backbone_ckpt:
          backbone_ckpt = backbone_ckpt["model"]
      backbone.load_state_dict(backbone_ckpt)
      backbone.eval()
      backbone.requires_grad_(False)
      self.backbone = backbone
      self.num_verts = cfg.num_verts
      self.input_size = cfg.input_size
      self.flipindex = cfg.flipindex.copy()
      self.data_root = Path(cfg.root_dir)
      txt_path = self.data_root / "resources/projection_matrix.txt"
      self.M_proj = np.loadtxt(txt_path, dtype=np.float32)
      M1 = np.array(
          [
              [400.0, 0, 0, 0],
              [0, 400.0, 0, 0],
              [0, 0, 1, 0],
              [400.0, 400.0, 0, 1],
          ]
      )
      self.M1 = M1
      camera_matrix = self.M_proj @ M1
      camera_matrix = camera_matrix[:3, :3].T
      camera_matrix[0, 2] = 400
      camera_matrix[1, 2] = 400
      self.camera_matrix = camera_matrix.copy()
      self.use_eyes = False
      if cfg.eyes is not None:
          self.use_eyes = True
          from eye_dataset import EyeDataset
  
          eye_dataset = EyeDataset(cfg.eyes["root"], load_data=False)
          self.iris_idx_481 = eye_dataset.iris_idx_481
  
imports: import os
  import time
  import timm
  import glob
  import numpy
  import os.path
  import cv2
  import torch
  import torch.distributed
  from torch import nn
  from pathlib import Path
  from backbones import get_network
  from skimage import transform
  from scipy.spatial.transform import Rotation
label_desc: Use image node_id 1 to create a new JMLRInference object from inherited base classes: nn with example: obj = JMLRInference(cfg, local_rank)